Abstract:

Directed acyclic graphs (DAGs) are commonly used to represent causal networks over a large number of random variables. We consider the task of estimating the equivalence class of a potentially highdimensional DAG representing a general linear structural equation model. By exploiting properties of common random graph families, we develop a new algorithm that requires conditioning only on small sets of variables. This is useful for highdimensional settings, and requires significantly less computation than current methods. In extending previous theoretical results for undirected graphs to the setting of DAGs, we prove the consistency of our algorithm, and generalize to a broader class of models than current methods. In low and highdimensional simulation settings, we demonstrate improvements over the stateoftheart alternative and conclude by applying our proposed algorithm on a real gene expression data set.
